code: def get_jit_fused_T5_layer_ff_forward():
      from transformers.models.t5.modeling_t5 import T5LayerFF
  
      def forward(
          self: T5LayerFF, hidden_states: torch.Tensor
      ) -> torch.Tensor:
          forwarded_states = self.layer_norm(hidden_states)
          forwarded_states = self.DenseReluDense(forwarded_states)
          hidden_states = self.dropout_add(
              forwarded_states,
              hidden_states,
              self.dropout.p,
              self.dropout.training,
          )
          return hidden_states
  
      return forward
  
file_path: ColossalAI/colossalai/shardformer/modeling/t5.py
imports: import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.nn import CrossEntropyLoss
  from torch.utils.checkpoint import checkpoint
  from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 2 for calling a global function with example usage: get_jit_fused_T5_layer_ff_forward() and returns: forward, hidden_states